Whatif

Carry offered taxonomic edges of a superseded model forward to its current successor. edges lists 1 to 25 edge ids that sim_migration_offers(model) offered; pass successor (the current it returned) so a different current successor — a re-point or a chain that grew since — is refused rather than followed. Every edge is checked before anything is written (it must touch model, be offered, pass sim_li

Record a checked connection between two stored models' declared ports, fromModel's fromPort (a place tagged port.output) feeding toModel's toPort (a transition tagged port.input), as a content-addressed Binding (ROADMAP.md Phase 9/11f). Checked now, by the same binds-port-v1 rule sim_run_pipeline applies: both ports exist with the right direction and element kind, the output is a token place, the

Calibrate a model against YOUR event log — the reading that meets reality. Upload CSV (case_id, activity, timestamp; the shape sim_dataset emits, activities = transition ids), and rates are learned from the observed timings: sources from inter-arrival times, services from the gap before their completions, all per hour. Instant-pickup transitions (declared rate >= 100) keep their declared rate — th

Tell whether two differently-labelled models are actually the same net: an isomorphism-invariant id computed from the model's EXACT automorphism orbits (orbits.go), not the colour-refinement (WL) kind sim_classify falls back to when the exact search can't decide. Two models differing only by renaming places or transitions share the same canonicalId even though their content-addressed ids (from sim

Re-check a stored structural witness against both stored models with the independent checker (pkg/morphism), which does no search. Given a structural edge's id, checks every witness of that edge; an edge with none answers unwitnessed (unproven, not disproved). Verdicts: valid, invalid (the map is not a morphism of the stored models), or uncheckable (a model was deleted, a net is malformed, or the

Discover the parameter classes of a stored model and return them as JSON-LD with empty annotation slots for you to fill in (label, comment, unit, domain, substitutes — nothing else; membership/kind/evidence are derived and settled by measurement, not yours to edit). With verify=true a shared colour is checked by exact automorphism proof where the search can decide it (settling interchangeability o

Derive a Petri-net model from source code with the configured LLM (control-flow, state-machine, resources or concurrency focus) and store it as a NEW model you own when it passes sim_create_model's decode and the model validator. Stored, it returns stored: true with id, url, name, usage and warnings (what sim_create_model would warn about the model, omitted when there is nothing to say). Otherwise

Run several scenarios against one model on one shared seed and return them side by side — the seed sharing is server-enforced, so differences are the scenarios, not the dice. Returns a summary by default (finals with finalStdDev, each final count's spread across realizations, throughput/mean/P95 metrics, depletion and contention — no time series); pass full=true for the complete trajectories, whic

List the component registry: pre-baked subnet templates (arrivals, service, hazard, inventory, decision, mailbox, datastore) with the calibration discipline baked into the arcs and rates. Each entry names its ports (places you can attach onto existing places), its params with recommended defaults, and the discipline notes explaining WHY the template is shaped the way it is. Compose them with sim_c

Instantiate a registry component into a model and store the result as a NEW content-addressed model you own (lineage recorded when composing onto an existing id). Omit id to start a model from the component alone; pass attach to fuse a component port onto one of the model's existing places (e.g. attach {"queue": "tickets_queue"} wires a hazard onto the service's queue). Prefix namespaces the creat

Check how well a stored model matches an observed event log WITHOUT rewriting its rates — the read sim_calibrate bundles into calibration, offered on its own and in full: fitness (can the model replay each case?), precision (does it allow behaviour never observed?), generalization and simplicity, with per-trace diagnostics naming the activities that could not fire. Also returns a residual block: t

Online conformance: replay an ORDERED event stream against a stored model and get a running verdict — does the model still match, or which event was the first that did not, and why. Unlike sim_conformance (whole finished cases, aggregate fitness) the order of events IS the stream's order, cases may interleave, and the answer names the first non-conforming event: its position, case, activity, wheth

Mint a named Collection you own and return its content id. A collection carries no member list of its own — a mutable list would change the collection's own id every time something joined it, the same reason Lineage lives beside a model rather than inside it. Add members with sim_link(collectionID, "hasMember", memberID) — members must be stored entities — and read them back with sim_neighbors(col

Store a Petri-net model and return its content id and url, plus warnings (omitted when there are none) for a model that stores but probably does not say what was meant: transitions with no rate, which simulations run at the engine default of 1 per hour (each stage of a staged one, in a run without a schedule); a net in which nothing can fire at its declared initial marking; a guard no simulation e

Define a System (ROADMAP.md Phase 11f): a content-addressed, stored set of models and the bindings between them, so the catalog can answer questions about them together. Pass collection (snapshot its model and binding members now; other members are listed under skipped, and a grown collection is a new system, the old id still readable) or models and bindings (JSON arrays of ids), not both. Every b

Run every applicable READING of a model against the others and report agreement or divergence with the reason: discrete SSA means vs the continuous mean-field solve, algebraically derived conservation laws vs simulated means, and (for game-schema models) the closed-form incidence ranking vs rollouts vs exact search. Divergence is a finding, not an error — small-count mean-field gaps and the prior'

Generate a synthetic event log from a stored model (seeded SSA playout; case-per-arrival). Returns CSV. Deterministic: same id, same seed, same bytes.

Delete a model you created signed in. Refused for the curated catalog, for models you do not own (a model created without sign-in is nobody's, for good), and for models already dedicated to the commons (a dedication is irrevocable).

Returns what one more of each resource (and a change in each rate) is worth to the outcome, each ranked against a measured noise floor — no fitness test to write. Also reports generic gates (mass balance, dormant sources, staffing knee and practical knee, whether any knob binds), parameter classes among the controls, and four structural readings needing no run (T-invariants, siphons/traps with dea

Structural difference between two stored models: places, transitions and arcs added or removed, and surviving elements whose numbers changed (initial, capacity, rate, stages, arc weight or kind). The readout for what a builder turn, a sim_extend or a sim_refine actually changed between two ids in a lineage.

Distill exact search into the play scorer: fit rate multipliers for named transition groups so play's rankings agree with exact minimax, on positions sampled by random self-play and labeled by search. This is TACTICAL calibration — the counterpart of sim_calibrate, which learns rates from an event log. The division of labor is deliberate (petri-pilot experiments/ode-minimax): structure carries the

List every relation touching an entity, as either subject or object. Answered from an index by subject and object; each call re-lists the store so edges written by other instances are seen. Legacy edges whose predicate predates the registry are included with axis "unregistered". Structural edges (isomorphicTo, subnetOf, preservesInvariant, refines) list the witnesses that prove them; one listing n

Score a player's legal next moves with the PLAY method (the blog post's move picker): apply each candidate hypothetically and score the expected objective of the position it leads to, by seeded SSA rollouts (seed 11, shared across candidates so a difference measures the move, not the dice) — the best move scores highest. Needs the game schema (simulation.objective + simulation.players). The respon

Apply structural edits to a stored model and store the result as a NEW model you own, with lineage back to the original — the same vocabulary the guided builder uses behind its interview, now callable directly. Operations (JSON array, each with "op"): add_place {id, initial}, add_transition {id, guard, event}, add_arc {from, to, weight, kinetic, type}, remove_place, remove_transition, remove_arc {

Fetch a stored prompt or artifact by id as JSON-LD: the two records sim_prompt and sim_reroll store besides a model. An artifact (@type Artifact) is an LLM reply that was not stored as a model — a report, code, or model JSON that failed validation — returned whole as content (the store caps an artifact at 262144 bytes), with contentKind. A prompt (@type Prompt) is what was asked: text, system, llm

Fetch a stored Binding by id, with its check computed now (binds-port-v1; binds-port-v2 for a binding that declares a unit conversion, which is returned as convert; binds-port-v3 for one that declares a delay, returned as delay in hours): ok (with the unit verdict: agree, converted or unchecked), refused (a binding recorded before the check that sim_bind and sim_run_pipeline would refuse today, fl

Fetch a stored model's full Petri-net JSON by its content id. A name in place of an id is refused saying to search with sim_list_models' query, and that a model created without sign-in is listed for nobody, so its id is the one its create returned; or naming the curated model's id when a curated model has exactly that name. An id of another stored kind (a prompt or artifact, a sim_reach question o

Derive a model's full algebraic invariant structure: conservation laws (Farkas P-invariants — weighted place sums every run preserves, the arithmetic a trust panel should show), firing cycles (T-invariants, named per-cycle with a readable detail sentence, each tagged StructuralProof), and the siphon/trap report (every minimal siphon and trap found from the arc structure, plus deadlock witnesses —

Dedicate a model you created to the commons under CC0-1.0, CC-BY-4.0, CC-BY-SA-4.0. It then appears in every user's listing with the license shown, and the dedication is IRREVOCABLE — it cannot be changed or deleted afterwards, which is what makes it safe for others to build on. CC0-1.0 is the cleanest choice for a model: attribution terms are hard to honor for a net someone folds into a larger on

Record a typed edge between two stored entities (models, prompts, artifacts, maps, collections, bindings, relations — not witnesses), using a registered predicate. sim_link writes these (subject → object): @type (model → a https://sim.pflow.xyz/ns/models/v1 type name); about (artifact/prompt/map/collection → model/collection/binding/system); broader (model/collection/system → model/collection/syst

List the content id of every stored Binding.

List the models visible to you, as a JSON array of {id, name, description, license, curated, mine} sorted by name (a field that does not apply is left out): the curated catalog (curated: true), models dedicated to the commons (each carries its license), and your own (mine: true). The curated models are complete, working examples of the model JSON: read one with sim_get_model and copy its shape whe

Fetch a stored Map's key->value data by id.

List the content id of every stored Map.

Store a key->value lookup table as its own content-addressed entity — a generated parameter sweep, a rate table, a component registry, anything shaped as key->value rather than free text (an artifact) or a Petri net (a model). Returns its content id; the same data, even with keys inserted in a different order, returns the same id.

List what happens to the edges of a superseded model. Taxonomic edges (hasMember, @type, broader, related, closeMatch, cites, about) are OFFERED: each row gives the edge as it would read on the model's current successor (followed through its supersession chain), whether sim_link's checks pass for it now (passes, refused, or unchecked when the store could not answer), any earlier migrations of it,

List the sheets this user has published, with their URLs.

One-hop traversal from an entity, read through the predicate registry. With no predicate: the object of every relation where the entity is the subject. With a predicate: the entities one hop along it — for an inverse name that is never stored (narrower) the stored predicate is read backwards (the subjects of the broader edges pointing at the entity), and for a symmetric one (related, closeMatch) b

Multi-objective optimisation over transition rates for a stored model: Monte Carlo samples the rate ranges, runs each combination to the horizon with the continuous engine, and returns every sample with a Pareto flag — the non-dominated set is the trade-off frontier ('which staffing is non-dominated on served vs walked out'). Continuous reading: a model with a schedule or a gate is refused with th

Two-rate grid for a stored model: vary two transition rates over ranges, run each combination to the horizon with the continuous engine, and return the observable's final value as a grid — 'which regime of arrivals × restock keeps the queue empty'. Continuous reading: a model with a schedule or a gate is refused with the reason. Costs 1 token per 50 grid cells (rounded up; a 40x40 grid empties the

Ask an LLM to derive something from a stored entity: a variant model, a report, a piece of generated code — whatever the prompt asks for. The parent's JSON rides along as context after a fixed instruction: answer a request for a new or changed model with only the complete model JSON (the shape sim_create_model takes), and anything else in plain text or markdown. The parent is looked up as a model

Propose candidate @type values for one or more stored models, e.g. "QueueingSystem" or "ResourcePool", from a small Diagnose run this tool performs on each model (nothing is cached or reused between calls). Every rule is a hand-written assumption about what a shape of knobs/loss/siphons/classes tends to mean, not a structural proof or a measurement, so results are ASSUMPTION-grade until a human re

Prove a structural relation between two stored models and record it with its witness. isomorphicTo: an exact canonical-labelling search (the one behind sim_canonical, seeded with exactly what the check compares) finds a place/transition bijection preserving arcs (direction, type, weight, kinetic), place sort, initial marking, capacity, rate or delay and refine.* tags. It does not compare guards, s

Publish a stored model into the signed-in user's Google Sheets: the model workbook (live formulas when honest, a refusal tab when not), a server-run scenario as data tabs, and trajectory + contention charts. Returns the sheet URL. Counts against the daily quota, and costs 1 token from your account's compute bucket for the run.

Publish the generated application for a model you own — the single-file HTML a generator produced from the model's `view` prompt. Served at /app/<id> in a sandboxed opaque origin (no cookies, no session; only the CORS-open public API is reachable). START FROM THE RUNTIME, not from scratch: /lib/app-template.html is a working console that imports /lib/sim-console.js and composes <sim-controls>, <si

Publish a multi-scenario comparison into the signed-in user's Google Sheets — sim_compare's export, the counterpart of sim_publish for a single scenario. Runs every scenario on one shared seed (the same server-enforced sharing sim_compare uses, so differences are the scenarios and not the dice) and writes a comparison table plus a trajectory chart, rather than one scenario's own data tabs. Returns

Answer a question over the edge graph between stored models: bounded paths, closure over transitive predicates, and conjunctive patterns of up to 4 triples joined on shared ?variables. The query argument is JSON: {"select": ["?m"], "where": [{"subject": "?m", "predicate": "subnetOf", "object": "<content id>", "mode": "closure"}, {"subject": "?m", "predicate": "@type", "object": "QueueingSystem"}]}

Returns whether the marking to is reachable from the marking from in ONE stored net (a model, or a system that is one net): yes with a firing trace, no with a re-checkable P-invariant (or after an exhaustive search), or unknown with every reason. A sim_compose result is one fused net, so it qualifies; a sim_bind binding or a collection links rates, not tokens, and answers unknown (not-one-net). A

Run a seeded scenario and get back the result PLUS a signed run receipt: an Ed25519 certificate over (model id, scenario, result hash, service revision). Anyone can check it two ways — verify the signature offline against the embedded public key (proves this service reported this result), and POST it to /api/receipts/verify (no auth) to replay the run and confirm the result hash reproduces (proves

Refine a model's parameter classes by editing what the model SAYS (tags on a place or transition, or assertedClasses), then re-derive. Returns a NEW model id (ids are content addresses, so the original stays reachable) plus a before/after class diff. tags can only split classes; assertedClasses declares a merge and gets re-verified and costed, never trusted blind. Read the sim://docs/classificatio

Re-run a stored sim_prompt against the SAME parent it originally ran against — a sibling attempt, never a chain: it never derives from the previous attempt's output, only from the original parent, so rerolling ten times leaves ten independent siblings in lineage rather than a chain of ten. Reuses the original prompt's text and system unless you override them here; a prompt with no requestTemplate

Returns one scenario result per model that a set of stored Bindings (see sim_bind) touches, run as a composed pipeline (each result's contention split into contended, waitingForWork and neverFired exactly as sim_scenario's is), with the assumptions the composition makes, inTransit (per delayed binding, the source's flow its delay carried past the horizon) and, when the set holds a loop, the loop's

Run a seeded what-if scenario against a stored model: marking overrides, rate overrides, piecewise rate schedules, and params assignments to the model's declared structural parameters (arc weights, capacities — batch sizes and shelf sizes). Pure read — asking cannot change the model. Returns trajectory, final marking, metrics, depleted, contended, waitingForWork, neverFired (all three described be

Mark an old version of your model as replaced by a newer one. The old id keeps working and its commons dedication (if any) stands — only the listing moves on to the successor. Both models must be yours. It also records a supersededBy edge (old → new), so sim_edges(old) and sim_neighbors(old, "supersededBy") show that the supersession happened. Re-pointing an old model to a different successor keep

Returns a stored System's view ({id}), the models whose ports can bind to one declared port ({model, port, direction?, among?}), or the stored systems that name a model or a binding ({naming}); one form per call. {id}: the system's view, computed now and never stored (system-view-v2): each model (present, superseded), each binding with its binds-port check (v1, or v2 with its convert when it decla

Verify declared properties of a stored model: a report with one verdict per property, proved, refuted or unknown (unknown is never a pass, and ok is true only when every property is proved), with evidence for a proof and a counterexample for a refutation, plus caveats naming anything the analysis net could not express. Unset properties check bounded and deadlock-free. Every check starts from the m